Real Time Streaming with Apache Flink and Kafka: Simple Example

  08 Dec 2016

Last Tuesday I attended the Apache Flink Meetup here in London for a coding dojo. The previous coding dojo was really very interesting and I went away with some good learnings - it also provided me with motivation to look a bit more into Apache Flink and I eventually published two blog posts on this topic. So I was quite excited to attend this new coding dojo event. Unfortunately this time round the attendency rate was not so good, however, event organiser Ignas Vadaisa and I still went ahead and tried to get a very simple Kafka and Flink setup going, the result of which is discussed here.

The main idea was to set up a simple Kafka Producer (Ignas wrote a Scala object which sends a random pick from a set of words to a Kafka topic), I set up a local installation of Kafka and wrote a simple Kafka Consumer, which is using Flink to do a word count.

I will talk you through the setup now, which is rather straight forward and should be a starting point for furhter exploration:

Check the scala version you have installed

$ scala -version

Download Kafka from here and extract the file in a convenient directory. Make sure you choose a Kafka version which goes in line with your Scala version!

Next we follow mainly the Kafka Quickstart guide:

Start Zookeeper:

$ bin/ config/

Start Kafka:

$ bin/ config/

Create a topic: This is not strictly necessary, as our code (KafkaProducer) will take care of this any ways.

$ bin/ --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic words

List the topic:

$ bin/ --list --zookeeper localhost:2181

Let’s first create the Kafka Producer. We will generate some random data and send it to the messaging queue:

import java.util.Properties
import org.apache.kafka.clients.producer.{KafkaProducer, ProducerRecord}
import scala.util.Random

object KafkaProducer extends App {

  val topic = "words"
  val props = new Properties()
  props.put("bootstrap.servers", "localhost:9092")
  props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer")
  props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer")

  val rnd = new Random()
  val wordSet = Seq("Dog", "Cat", "Cow")
  val n = wordSet.length

  val producer = new KafkaProducer[String,String](props)

  var key  = 0

    val index = rnd.nextInt(n)
    producer.send(new ProducerRecord(topic, key.toString, wordSet(index)))
    key = key + 1


Let’s run this code. To understand if our code is actually working, let’s use a Kafka utility to check if there are any messages in the queue:

Show streaming data in the Kafka topic:

bin/ --zookeeper localhost:2181 --topic words

Since the setup is working so far, we can focus on creating the KafkaConsumer now. In the snapshot 1.2 Flink provides quite few connectors to external data sources and stores, which makes it quite straight forward to source data from and load data to such stores:

import java.util.Properties
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.streaming.connectors.kafka._
import org.apache.flink.streaming.util.serialization.SimpleStringSchema

object KafkaConsumer {

  def main(args: Array[String]) {

    val env = StreamExecutionEnvironment.getExecutionEnvironment

    val properties = new Properties()
    // comma separated list of Kafka brokers
    properties.setProperty("bootstrap.servers", "localhost:9092")
    // id of the consumer group
    properties.setProperty("", "test")
    val stream = env
      // words is our Kafka topic
      .addSource(new FlinkKafkaConsumer010[String]("words", new SimpleStringSchema(), properties))


    env.execute("Kafka Window Stream WordCount")

In this first attempt we simply retrieve the data from the Kafka queue and print the values out to the console. Try to run this code.

Next let’s count the words in five seconds intervals:

    val counts = stream
      .map { (_, 1) }


Execute KafkaConsumer now and enjoy the window aggregation! Granted this was a very simple setup, so feel free to explore more of the Apache Flink world and create an advanced example. Have fun!

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